Learning Issues Research Articles

Family Functioning and Child Participation Following Early Childhood Traumatic Brain Injury

Family functioning and child participation are critical factors in recovery from traumatic brain injury (TBI) and academic and social outcomes. Using mixed methods, we investigated these factors in children who experienced early childhood TBIs and then entered formal schooling. Caregivers completed interviews and scales of family functioning and child participation. Their scores did not reflect problematic family functioning, but showed participation across settings was below average. Themes from interviews included: (1) early childhood TBIs can affect family functioning even years after the injury; (2) learning, academic, and social-emotional issues impact participation and family functioning following early childhood TBI; and (3) appropriate supports and accessible environments are critical for both family functioning and child participation across settings. Mixed methods analyses indicated convergence, divergence, and expansion between qualitative and quantitative results. Results enhance our understanding of challenges encountered after early childhood TBI and provide directions for future interventions.

An advancement in AdaSyn for imbalanced learning: An application to fraud detection in digital transactions

Imbalanced Learning is a significant issue in machine learning, affecting the performance and accuracy of binary or multi-classification algorithms, especially in large-scale data handling and classification. There are some popular techniques to covert this imbalanced data into a balanced one such as undersampling, under-sampling with tomek links, randomized oversampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic generation (ADASYN). Generally, the ADASYN algorithm could be used to propagate minority sample points to rise the imbalanced ratio between majority and minority sample points, but in some cases, it may conflict with decision boundary points and noisy points. This paper proposed a Refitted AdaSyn Algorithm (RAA) with Gaussian Distribution (GD). So that new minority samples are distributed much closer to the center of the minority sample to spotlight the conflicts. The classification accuracy has improved with RAA over formal ADASYN. For examining the proposed work the imbalanced benchmark datasets like European, Banksim, Paymentcard, and UCI credit card are considered. Vanilla Generative Adversarial Network (GAN) is a deep learning model used to classify fraud and non-fraud transactions, demonstrating significant differences between balanced and imbalanced learning approaches and achieving an accuracy of 97.5% on dataset DS4.

Distancing skills in remote work: narratives about HRD managers support organisations

Purpose This paper aims to identify the essential skills required by Human Resource Development (HRD) professionals to effectively respond to the various modes of labour organisation implemented due to the mandatory lockdown imposed by the Portuguese government in March 2020 as well as implications for learning and development (L&D) issues. Design/methodology/approach Data is based on 34 semi-structured interviews with HRD professionals from companies in different activity sectors. Findings The results reveal that the COVID-19 pandemic has brought out the development of socio-emotional skills among HRD professionals such as creativity, improvisation, self-exploration, innovation, collaboration, team spirit, resilience, flexibility, problem-solving, adaptability, priority management, emotional intelligence, social influence, social contact, interpersonal relationships, communication and online learning development. Research limitations/implications It will be interesting for future research to explore “what” and “how” HRD managers are planning, organising and implementing training and development plans to improve the skills of remote workers, which tend to grow in a post-pandemic COVID-19 phase. Practical implications This research emphasises the importance of HRD managers’ role in better coordinating the work of employees who are physically distant from the company. It also highlights the need for different skills required for effective digital HRD, support and monitoring of remote employees. The results provide important inputs to design and implement effective L&D programs for professionals working remotely and to reinforce the HRD role in organisations. Originality/value The research is original for twofold reasons: 1) HRD professionals are usually not trained to manage remote workers, which also means that they probably lack the skills to take the most out of remote working models; and 2) HRD professionals and the HRD function need to address the skills required to successfully implement flexible forms of work organisation as well as to implement adequate L&D policies to answer remote work practices.

Verbal triggers of positive emotions as a tool for forming the emotional component of schoolchildren’s value attitude towards other people

Importance. Building students’ value-based attitudes towards other people is an important goal of the modern educational process. This kind of attitude includes three aspects (cognitive, emotional and behavioural). The research focuses on the development of the emotional aspect of the valuebased attitude towards other people. The author analyses how verbal means affect people’s emotions and how these means can be used to create positive emotions in the process of building the emotional aspect of the value-based attitude towards other people in schoolchildren.Research Methods. The study uses an analytical review of the scientific and methodological literature on pedagogy; lexical, morphological and stylistic analyses of verbal structures are used.Results and Discussion. Verbal triggers are designed by the author in order to evoke positive emotions in schoolchildren towards the people around them. The examples of exercises with verbal triggers of tenderness and admiration for extra-curricular classes, or course on emotional intelligence, or as part of academic lessons, are also designed by the author.Conclusion. The research results can be used in the process of organising complex work on the formation of the emotional component of the value attitude towards people, as well as for further theoretical research on the issues of emotional learning and moral education in general.

The Realities of Implementing First Language (L1) for Instruction in Lower Primary Schools in Ghana

In May 2002, Ghana approved the use of English as the language of instruction (LOI) in lower primary schools. In 2012 however, a new policy was declared on using the Ghanaian language, the first language (L1) of learners as LOI. This raised some controversies among stakeholders. Currently, although teachers are required to implement the new policy, there appears to be an apparent lack of literature on what really pertains in the lower primary classrooms. This study provides preliminary findings from a larger study funded by the Jacobs Foundation. The study used a concurrent mixed methods design. In the qualitative phase, 48 participants were interviewed, and their lessons were observed using interview and observation guides. In the quantitative phase, 448 respondents completed a questionnaire. Key findings indicate that while teachers recognise the benefits of using L1 to enhance engagement and comprehension, they often adopt a blended approach that combines both English and L1. Major challenges in implementing the L1 policy include community-parental resistance, unavailability of teaching and learning resources, policy-examination mismatches and issues with teacher postings. Subsequently, participants advocated for a blended LOI approach because of its added merit to help learners understand concepts more easily in their native languages while developing crucial skills in English, and further ease the transition of L1 at the lower primary to English at later years. It is recommended that policymakers should promptly investigate the potential benefits of adopting a blended approach to modify existing policies that mandates the use of L1.

AI and memes: Across the boundaries

Memes have become one of the most popular cultures encapsulated by digital artifacts across digital spaces of all kinds. Users can create memes by themselves but can also use online services or tools. Among these, AI tools are popularly used because they can quickly generate creative memes by exploring vast stores of preexisting resources. Depending on the configurations of their algorithms and storehouses of resources, different AI meme generators can create different memes with the same prompt; further, the same generator can create different memes at different times with the same prompt. In addition, AI-generated memes may include controversial content through images and captions or other components, unanticipated by the writer of the prompt. In this study, we will examine several AI meme generators by creating memes using identical prompts and a series of prompts and compare the outputs, with a focus on cross-world ethical issues through the theoretical lens of information domains (Burnett, 2015; Burnett & Burnett, 2019). The results will provide insights into learning, meme generation, and ethical issues related to the use of AI tools.

Quarta: quantum supervised and unsupervised learning for binary classification in domain-incremental learning

Quantum machine learning recently gained prominence due to the promise of quantum computers in solving machine learning problems that are intractable on a classical computer. Nevertheless, several studies on problems which remain challenging for classical computing algorithms are emerging. One of these is classifying continuously incoming data instances in incremental fashion, which is studied in this paper through a hybrid computational solution that combines classical and quantum techniques. Hybrid approaches represents one of the current ways for the use of quantum computation in practical applications. In this paper, we show how typical issues of domain-incremental learning can be equally addressed with the properties of quantum mechanics, until to offer often better results. We propose the framework QUARTA to combine algorithms of quantum supervised learning, that is, variational quantum circuits, and techniques used in quantum unsupervised learning, that is, distance estimation. We aim at keeping the classification capabilities, which have learned on previously processed data instances, preserved as much as possible, and then acquiring new knowledge on new data instances. Experiments are performed on real-world datasets with quantum simulators.

Digital Literacy: A Strategy for Leveraging Skills Development

The main intention of this scientific article was to review the technical skills and social skills achieved with digital literacy in students of the San Isidro Rural Educational Institution in Santa Rosa de Osos, Antioquia, Colombia. It was sheltered under the theoretical postulates of Area, Gutiérrez and Vidal (2012), Cornachione (2006), Fonseca (2011), among other experts in the area that is developed; The study on digital literacy is framed in positivist thinking, since the variable was measured to compute derivations, then it was verified by descriptive statistics in which sequelae were compared with the assumptions and then, to outline the offer that aims to convert what is shown in the present article. The population of this study was eighty-two (82) individuals, including pedagogues and schoolchildren from the San Isidro Rural Educational Institution in Santa Rosa de Osos, Antioquia, Colombia. Specifically, the essence of the dissertation is that, once the corresponding digital literacy has been carried out in the students by the educational students, the progress of teaching skills is achieved through the diligence of actions using the Chamilo virtual medium, cognitive learning issues will be optimized, as well as educational progress in the educational entity.

Socio-scientific issues-based electronic-learning material reveals a high incorporation of lifelong learning, ethical, and sustainability issues

Socio-scientific issues-based electronic-learning material reveals a high incorporation of lifelong learning, ethical, and sustainability issues

A Patch Diversity Transformer for Domain Generalized Semantic Segmentation.

Domain generalization (DG) is one of the critical issues for deep learning in unknown domains. How to effectively represent domain-invariant context (DIC) is a difficult problem that DG needs to solve. Transformers have shown the potential to learn generalized features, since the powerful ability to learn global context. In this article, a novel method named patch diversity Transformer (PDTrans) is proposed to improve the DG for scene segmentation by learning global multidomain semantic relations. Specifically, patch photometric perturbation (PPP) is proposed to improve the representation of multidomain in the global context information, which helps the Transformer learn the relationship between multiple domains. Besides, patch statistics perturbation (PSP) is proposed to model the feature statistics of patches under different domain shifts, which enables the model to encode domain-invariant semantic features and improve generalization. PPP and PSP can help to diversify the source domain at the patch level and feature level. PDTrans learns context across diverse patches and takes advantage of self-attention to improve DG. Extensive experiments demonstrate the tremendous performance advantages of the PDTrans over state-of-the-art DG methods.

Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning.

Given a model well-trained with a large-scale base dataset, few-shot class-incremental learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously. Currently, semi-supervised learning technique that harnesses freely available unlabeled data to compensate for limited labeled data can boost the performance in numerous vision tasks, which heuristically can be applied to tackle issues in FSCIL, i.e., the semi-supervised FSCIL (Semi-FSCIL). So far, very limited work focuses on the Semi-FSCIL task, leaving the adaptability issue of semi-supervised learning to the FSCIL task unresolved. In this article, we focus on this adaptability issue and present a simple yet efficient Semi-FSCIL framework named uncertainty-aware distillation with class-equilibrium (UaD-ClE), encompassing two modules: uncertainty-aware distillation (UaD) and class equilibrium (ClE). Specifically, when incorporating unlabeled data into each incremental session, we introduce the ClE module that employs a class-balanced self-training (CB_ST) to avoid the gradual dominance of easy-to-classified classes on pseudo-label generation. To distill reliable knowledge from the reference model, we further implement the UaD module that combines uncertainty-guided knowledge refinement with adaptive distillation. Comprehensive experiments on three benchmark datasets demonstrate that our method can boost the adaptability of unlabeled data with the semi-supervised learning technique in FSCIL tasks. The code is available at https://github.com/yawencui/UaD-ClE.

Exploratory Study On The Impact Of Online Virtual Classroom Education On Physical & Mental Health And Development Of Social Skills Of Children

Background: This study was done in the context of the sudden shift of educational system from the conventional classroom environment to virtual schooling at home through online teaching during the COVID-19 pandemic. Substantial literature has brought out the positive and negative impacts of virtual schooling; for example, few of the positive effects are increase in social connections using the technology, helps in accessing resources and knowledge, children are more safer at home, etc. However, the negative impacts are more severe, particularly, on the health of the children participating online classes through electronic devices. As the children spend most of their active time indoors and watching the small, lighted screen the children are prone to physical and mental health issues. In this context, this study attempts to explore the physical & mental health issues, social behavioural problems, and learning issues of children participated online classes. Materials and Methods: In this exploratory study, cross-sectional survey method and quantitative analytical process were applied. Data were collected from 200 children in the age group of 6 to 18 years both male and female and urban and rural locations, who participated the online classes. A structured closed-end selfreporting questionnaire was used. Results: This study established the negative effect on physical health, mental health, and learning & participation issues of children of online virtual schooling. Findings confirmed the significant and positive relationship between the screen time and the various issues experienced by the children. Gender, age, and location type differences were indicated

Using machine learning models to detect artificially generated images

The use of machine learning models to classify real and artificially generated images is becoming an increasingly relevant area of research in the field of artificial intelligence. This paper is dedicated to the analysis and development of various machine learning models used for this task. Our main goal is to investigate the effectiveness of different neural network architectures in classifying images that have been created both in by human and with the help of artificial intelligence. As part of the study, we used nine different neural network architectures designed to analyse their effectiveness in classifying images. We studied both real images and those generated by artificial intelligence. The latter are becoming more and more common in the modern world, which means that there is a need for their identification and classification. The main results of our study include a detailed analysis of the characteristics of the generated images, as well as a comparison of the models using various metrics. We used metrics such as accuracy and F1-score, which allowed us to objectively evaluate the performance of each model. In addition, we identified the most effective models for the image classification task. One of the key findings of our study is that models that use data regularisation and augmentation performed better. Data regularisation and augmentation help to ensure more stable classification accuracy and reduce the tendency to overfitting, which is an important issue in machine learning. These results can be useful for developing strategies to counteract disinformation, which has become an issue in the modern information society. By using machine learning models, artificially generated images can be detected and separated from real images, which can help prevent the spread of fake news and disinformation. In addition, the results of our research can contribute to the further improvement of image generation algorithms, which is an important area of artificial intelligence development. They can serve as a basis for creating more accurate and efficient models that can generate images that are as close to real life as possible.

Language Learning Issues of Non-Reader in Rural and Urban Senior High School: A Comparative Analysis

The general objective of this study is to assess the language learning difficulties of non-readers in rural and urban senior high schools in the Division of Cotabato City in the School Year 2022-2023. The purpose of this study is to examine the effects of environmental context, including resource and teacher support, on language development of the learners with poor literacy skills. The research design used was qualitative and the study used a thematic analysis to elicit the experiences of 30 non-readers from rural and urban schools. The data was collected through interviews, focus group discussions and classes observation. The research shows that non-readers in the rural and urban areas have basic problems, including inadequate literacy skills and poor participation in language learning activities. However, there was inequality in the distribution of resources and the level of support received by teachers where urban schools were found to have better resources and teachers’ training than the rural ones. Other factors that added to the difficulties of non-readers, mainly the home environment and peer relations, were worse in rural areas. Therefore, the study underlines the necessity of developing specific measures to approach the issues of non-readers. The enhancement of the vocabulary, resource distribution, and teacher training should be the strategies that aim at eradicating the inequality that exists in the education systems especially for students with different literacy levels or those in different geographical locations

Long-term food supplementation with sweet basil (Ocimum basilicum L.) prevents age-associated cognitive decline in female mice.

Background: Mild cognitive decline, a common issue in aging, affects memory, learning, and attention. Nutrition can influence cognition, and research indicates that Ocimum sp. (holy basil and sweet basil) leaf extracts may enhance cognition in rodents and humans. However, these studies do not address whether these benefits extend to fresh or dry leaves consumed in typical human diets, along with physiological aging. Aim: To investigate the effects of sweet basil supplementation on cognition in mature and aged female mice. Methods: Female C57bl mice were divided into four groups: 8-month-old mature adults and 18-month-old aged adults, each receiving either a control or supplemented diet. The supplemented diet included a mix of standard chow and fresh basil leaves, administered for 2-8 months. Cognitive and behavioral assessments were conducted using the novel object recognition (NOR), Morris water maze (MWM), and elevated plus maze (EPM) tasks, focusing on memory, learning, and anxiety. Results: No cognitive improvement was observed in mature mice. However, aged mice receiving long-term basil supplementation showed enhanced discrimination in NOR and stayed closer to the absent platform in MWM compared to nonsupplemented controls. While aging mice exhibited reduced anxiety-like behavior in EPM, basil supplementation prevented this reduction. Conclusion: Basil supplementation appears beneficial in elderly mice, potentially preventing age-related cognitive decline and behavioral changes. These findings support the benefits of basil consumption in cognition and underscore its potential role in promoting healthy aging. Incorporating basil into the diet at a younger age may preserve memory and mitigate behavioral changes as individuals age.

Speaking and Individual Identity Reflection

Identity is a complex and multifaceted concept that encompasses an individual's sense of self, their unique characteristics, beliefs, values, and affiliations. It is a dynamic construct that evolves over time and is shaped by various internal and external factors, including personal experiences, social interactions, cultural background, and societal norms. This paper explores the various dimensions of identity and examines the role that identity plays in shaping human behavior, beliefs, and attitudes. It also discusses the challenges and opportunities associated with identity construction, as well as the potential implications of identity-related issues for teaching and learning.

Metaheuristic-driven space partitioning and ensemble learning for imbalanced classification

Imbalanced classification is a common issue in Machine Learning, particularly when misclassifying minor instances leads to significant costs. In literature, various strategies have been employed to address this problem. These include data-level, algorithm-level, cost-sensitive, and hybrid-level algorithms designed to tackle imbalanced problems. This paper aims to introduce a novel method that simultaneously enhances the ability of classification models to identify patterns more effectively and addresses imbalanced problems while minimizing alterations to the original data distribution. Our proposed framework combines ensemble learning, space partitioning, and the Synthetic Minority Oversampling Technique (SMOTE). This method decomposes the space into balanced sub-spaces and then trains an ensemble classifier based on these sub-spaces using a bagging approach. In the initial step, we develop a Space Partitioning by Metaheuristic algorithm (SPMH) to divide the space into multiple balanced subspaces. In the subsequent step, we present Imbalanced Classification by SPMH (ICSPMH) as a solution to imbalanced class problems. ICSPMH uses SPMH multiple times to divide the space into different sub-spaces, creating various sub-spaces each time. It then trains different classifiers for each portion of the space, creating an ensemble classifier through a bagging technique. To assess the performance of our proposed framework, we selected 44 well-known datasets for comparison with some state-of-the-art approaches. The results demonstrate that ICSPMH outperforms other competent methods and can potentially reduce the oversampling rate to zero. Additionally, an experiment indicated that the choice of metaheuristic algorithm in SPMH does not significantly impact the final performance. The paper also includes a correlation analysis between oversampling rate and final performance, revealing that the framework effectively eliminates imbalanced data problems with minimal changes to the original dataset. In summary, because ICSPMH applies fewer changes in data distribution and sets up local classifiers that improve classification performance, it looks like a promising method for classifying imbalanced datasets.

An Improved Divide-and-Conquer Approach to Estimating Mean Functional, with Application to Average Treatment Effect Estimation

Mean estimation is an important issue in statistical inference and machine learning. We are concerned with estimating mean functional that is a function of several nonparametric functions when there is a large amount of observations. Directly estimating such mean functional through nonparametric smoothing has the complexity of at least a quadratic order of the sample size, which is computationally prohibitive for massive data. The divide-and-conquer approach are thus readily used to alleviate the computational complexity issue, which however imposes a stringent condition on the sample size in each local machine if a locally optimal bandwidth is used. To address this issue, we suggest to use a globally optimal bandwidth in each local machine, which alleviates the restriction on the local sample sizes substantially. We show that the divide-and-conquer approach with a globally optimal bandwidth achieves the estimation efficiency bound as if all observations were pooled together. In terms of computational efficiency, our proposal outperforms the pooled algorithm dramatically. We demonstrate these properties through average treatment effect estimation from both the asymptotic and the non-asymptotic perspectives.

Current computational tools for protein lysine acylation site prediction.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

Development of The Search, Solve, Create, and Share (SSCS) Model with Strengthening Information Literacy for High School Students

This study aims to develop the search, Solve, Create, and Share (SSCS) Model with information literacy reinforcement to improve students' ability to write explanatory texts. This study used the research and development with the ADDIE model, which involved Analyze, Design, Develop, Implement, Evaluate. This study employs various data collection techniques, including interviews to understand learning issues, questionnaires to identify student needs and assess product validity, and tests to measure writing skills. The instruments used include observation sheets, questionnaires, interview guidelines, and scoring rubrics, all aimed at supporting the development of the instructional model. The results showed that the SSCS model with information literacy succeeded in improving students' skills that had been tested on 85 students in three high schools in Brebes Regency. The results showed that 16% of students received excellent ratings, 80% of students received good ratings, and 4% received fair ratings. Evaluations from teachers and students indicated positive acceptance of the model, although there were certain aspects which required further adjustments. This study provides a strong foundation for implementing the development of this model in the practice of learning to write explanatory texts in senior high schools.